Hemodynamic feature is an important clinical diagnostic parameter which provides critical information for cerebrovascular diseases. Currently, several approaches are applied to measure arteriovenous flow, including computed tomographic (CT) perfusion, magnetic resonance (MR) perfusion, phase-contrast magnetic resonance image (PC-MRI) and Doppler ultrasound. However, each of the above-mentioned technique does not fully satisfying the clinical requirements. For example, patient undergo CT imaging would need to expose to a high dose of radiation, with MR imaging would require a long image acquisition duration. Though Doppler ultrasound is a clinical routine vessel flow quantification method, the modality is limited due to its poor spatial resolution with significant attenuation after passing through skull. PC-MRI technique is a newly released flow quantification technique as a form of magnetic resonance angiography (MRA). The method allows an indirect measurement of in vivo blood flows with non-invasive and non-radiation exposure. However, the spatial-temporal resolution of PC-MRI is still limited, which could not effectively measure fast-changing and complicated flow situations.
Digital subtraction angiography (DSA) is a fluoroscopy technique that commonly used in interventional radiology. The term “subtraction” is referred as subtracting images with contrast medium by “pre-contrast images”. The technique is suitable for detecting cardiovascular and cerebrovascular arterial obstruction by visualizing blood flow with introducing of radiopaque iodine intravenously. Conventionally DSA is a clinical gold standard for arterial imaging for its high spatial and temporal resolution. The imaging method assesses hemodynamic vascular structures with only gray scales, including the detection of arterial occlusions, arterial stenosis, cerebral aneurysm and arteriovenous malformations. Several vendors have provided velocity color coding visualization software (Siemens Syngo iFlow, General Electric AngioViz, Philips 2D Perfusion).
Optical flow is a widely used computer vision algorithm that detects the pattern of apparent motion of objects, surfaces, and edges in a visual scene. The algorithm was firstly introduced by American psychologist James J. Gibson in the 1940s. The original concept was to study visual stimulus for movement perception, shape and distance perception, and control of locomotion. It is currently used by image processing for motion detection and object segmentation. Shpilfoygel et al. reviewed more than 100 manuscripts and illustrated the advantage of combining optical flow and DSA. Up to 2014, two major methods were applied in flow quantification, namely image density analysis and Lucas-Kanade optical flow method.
Another widely adopted Horn-Schunck optical flow method solve the velocity field with the minimization of global optical flow energy function. The method could linearly combine with Lucas-Kanade approach to include both local and global information. However, both Horn-Schunck and Lucas-Kanade method are developed on the basis of Brightness Constancy assumption. A large displacement of objects could cause the calculation error. A multiresolution strategy could be a solution to the problem.